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Connections between graphical Gaussian models and factor analysis

By M. Fátima Salgueiro, Peter Smith and John McDonald

Abstract

Connections between graphical Gaussian models and classical single-factor models are obtained by parameterizing the single-factor model as a graphical Gaussian model. Models are represented by independence graphs, and associations between each manifest variable and the latent factor are measured by factor partial correlations. Power calculations for the single-factor graphical Gaussian model are facilitated by expressing the manifest partial correlations as functions of the factor partial correlations. The power of selecting a graphical Gaussian model with an association structure between manifest variables compatible with a single-factor model is investigated. The results are illustrated using 2 examples: the 1st is a hypothetical factor model with parallel measures. The 2nd uses data from the British Household Panel Survey on job satisfaction

Topics: HA
Year: 2010
OAI identifier: oai:eprints.soton.ac.uk:154865
Provided by: e-Prints Soton
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